Multi-layer Incremental Induction

This paper describes a multi-layer incremental induction algorithm , MLII, which is linked to an existing nonincremental induction algorithm to learn incrementally from noisy data. MLII makes use of three operations: data partitioning, generalization and reduction. Generalization can either learn a set of rules from a (sub)set of examples, or reene a previous set of rules. The latter is achieved through a re-description operation called reduction: from a set of examples and a set of rules, we derive a new set of examples describing the behaviour of the rule set. New rules are extracted from these behavioral examples, and these rules can be seen as metarules , as they control previous rules in order to improve their predictive accuracy. Experimental results show that MLII achieves signiicant improvement on the existing nonincre-mental algorithm HCV used for experiments in this paper, in terms of rule accuracy.